Triple
T7202273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rose City Golf Course |
E148575
|
entity |
| Predicate | hasGreens |
P64312
|
FINISHED |
| Object | bentgrass or similar cool-season turf |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: bentgrass or similar cool-season turf | Statement: [Rose City Golf Course, hasGreens, bentgrass or similar cool-season turf]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGreens Context triple: [Rose City Golf Course, hasGreens, bentgrass or similar cool-season turf]
-
A.
hasGreenType
Indicates that an entity possesses or is associated with a type classified as green.
-
B.
hasVillageGreen
Indicates that one entity possesses or includes a village green as part of its area or facilities.
-
C.
hasGreenhouse
Indicates that an entity possesses or includes a greenhouse structure or facility.
-
D.
hasGreenSpaces
Indicates that an entity includes or is associated with areas of vegetation or natural greenery, such as parks, gardens, or lawns.
-
E.
hasGrassTypeGreens
chosen
Indicates that something possesses or includes green vegetation or grassy plant material.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c687e8cf188190b5f3ecffd681f04e |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e94a9ee4819086de79fcdfa1836a |
completed | March 27, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69c6e757fed4819091b0a096e3befc3a |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:52 p.m.